As a copper-containing polyphenol oxidase, laccase (EC 1.10.3.2), first isolated from the Japanese lacquer tree (Rhus vernicifera), is widely present in fungi, plants, and bacteria. It has shown considerable potential in food detoxification owing to its mild reaction conditions, broad substrate range, and environmental compatibility. This allows laccase to oxidize structurally diverse substrates, although catalytic efficiency varies with substrate structure, redox potential, and accessibility to the active site. Laccase can promote the degradation, transformation, removal, or detoxification of food toxic substances, including biogenic amines, polycyclic aromatic hydrocarbons, phenolic compounds, mycotoxins, and pesticide residues, through substrate oxidation initiated by electron transfer at copper-containing active sites. This review summarizes the mechanisms underlying laccase-mediated degradation, transformation, removal, and detoxification of food toxicants, together with optimization strategies including protein engineering, mediator regulation, and immobilization, supporting its development as a versatile biocatalyst for food safety control.

Molecular self-assembly provides a route to nanoscale structures with defined sizes and shapes. Protein cages assembled from repeated subunits form compartments that can package molecular cargoes. Although amino acid substitutions can alter intersubunit interactions and cage architecture, how protein backbone connectivity relates to the cage architectures accessible during laboratory evolution remains unclear. Here we evolved an artificial nucleocapsid from a circularly permuted variant of Aquifex aeolicus lumazine synthase (AaLS). Laboratory evolution increased the yield of purified nucleocapsids relative to the progenitor, and the evolved variant, NC119-3, packaged its encoding mRNA. Cryo-electron microscopy revealed a heterogeneous population of cages, within which we resolved 180- and 210-subunit cage families composed of 36 and 42 pentamers, respectively. Within each resolved family, particles with the same subunit stoichiometry differed in the arrangement and orientation of their pentameric building blocks. A 2.90 Å reconstruction of a tetrahedral 180-subunit cage further revealed that sequence-identical subunits adopt distinct conformations depending on their positions within the assembly. These pentamer-based architectures differ from the domain-swapped T = 4 cage previously reported for nucleocapsids evolved from an AaLS variant with a different circular permutation site. Together with previous work, these findings show that RNA-packaging nucleocapsids evolved from scaffolds with different backbone connectivity can adopt distinct cage architectures.

Droplet microfluidics underpins many single-cell, precision biomedical, and nanomaterial techniques. However, implementing these methods reproducibly remains challenging outside specialized laboratories due to high equipment costs, operational inflexibility, mechanical impracticality at the microscope stage, as well as open-source pumping solutions that are inadequately validated for low-flow stability. We present an accessible, research-grade, open-source droplet microfluidics platform integrating a compact, microfluidics-optimized microscopy stage with networked syringe-pump control. Designed for local digital fabrication, primarily 3D printing, and widely available components, the platform supports reproducible builds across diverse settings. This setup enables stable droplet generation, observation of rapid droplet dynamics, and automation-ready networked workflows. The microscopy module uses a Raspberry Pi camera and microsecond stroboscopic illumination to capture sharp images of fast-moving droplets without the need for a high-speed camera. A modular hardware interface and extensible, web-accessible software stack provide a path to integrate additional modules and laboratory automation beyond the configuration evaluated here. For infusion, we introduce an open-source syringe pump optimised for low reliable flow rather than maximum rates, combining gearbox-assisted stepper mechanics with workflow-centric control that enables manual intervention (keypad control) and remote operation (web interface and automation hooks). We benchmark droplet regimes, size tuning, and time-stability under low-flow conditions (between 25 and 2500 uL/h) relevant to advanced assays and demonstrate biomedical readiness through the encapsulation and incubation of cells, beads, and bacteria in monodisperse droplets. The platform has been replicated across laboratories in Latin America and globally, including independent builds, supporting reproducibility. All design files, firmware/software, analysis notebooks, and benchmark datasets are openly released.

Calcium phosphate biomineralization plays a crucial role in the formation of geological deposits, biological materials, and environmental remediation. While microorganisms are known to influence calcium phosphate precipitation, the relative contributions of bacterial cell surfaces and extracellular polymeric substances (EPS) remain poorly defined. This study presents the first microscale investigation of the distinct roles of Gram-positive and Gram-negative bacterial cells and EPS in regulating hydroxyapatite (HAp) mineralization, employing abiotic, EPS-mediated, and bacterial precipitation systems. Zeta potential measurements and Fourier Transform Infrared Spectroscopy (FTIR) revealed that both bacterial cells and isolated EPS provide negatively charged, chemically distinct interfaces capable of interacting with dissolved Ca2+ ions. Solution chemistry demonstrated greater phosphate and calcium ion depletion in biotic systems, consistent with enhanced crystal precipitation compared to abiotic controls. Field emission scanning electron microscopy (FESEM) and Brunauer–Emmett–Teller (BET) analyses showed that biological interfaces produced distinct crystal morphologies and surface characteristics. X-ray diffraction (XRD) revealed significant differences in phase composition: abiotic precipitation produced mixed calcium phosphate phases, whereas EPS-mediated precipitation promoted nucleation but remained dominated by precursor phases. In contrast, whole-cell systems favored HAp formation, with Sporosarcina pasteurii producing phase-pure crystalline HAp and Pseudomonas fluorescens producing HAp-dominant precipitates. Acid dissolution experiments further confirmed the increased stability of biogenic precipitates. These findings demonstrate that the spatial organization of bacterial surface functional groups, rather than surface charge alone, governs hydroxyapatite phase evolution, crystal growth, and mineral stability. While localized metabolic activity may contribute to microenvironmental conditions, the results highlight the importance of bacterial cell surfaces and EPS as key determinants of hydroxyapatite biomineralization, providing a mechanistic basis for the design of biologically mediated mineral materials.

Recent advances in biological foundation models have enabled new opportunities for data-driven molecular engineering. However, designing programmable RNA devices remains challenging because of the vast, nonlinear sequence-structure-function landscape. Here, we present Ernie-Switcher, a deep learning framework built upon the RNA foundation model (RNAErnie), comprising three coupled components: (i) integration of RNAErnie-derived sequence-phenotype representations for realistic design generation and filtering; (ii) characterization of RNA landscape navigability as a theoretical basis for sequence optimization; and (iii) incorporation of biophysical constraints to maintain optimization trajectories within biologically plausible bounds. We validated Ernie-Switcher using RNA toehold switches. Theoretically, the observed lower bound on threshold navigability (86.4%) of the RNA landscape supports the theoretical feasibility of RNA switch sequence optimization. Experimentally, the optimized toehold switches achieved ON/OFF expression ratios of up to 35-fold in E. coli. Collectively, RNAErnie-guided sequence optimization provides a scalable strategy for engineering high-performance RNA devices. To facilitate broad adoption, we deployed Ernie-Switcher as a freely accessible web platform.

Genomic-context models have shown that contextualizing protein embeddings can improve predictions of microbial function. We observe that a gene's protein embedding is partially predictable from the embeddings of neighboring genes in the genome, motivating high-ratio masking that requires reconstruction from sparser genomic context. Genomic modeling must also distinguish biological relationships from assembly conventions, since the ordering of separate contigs does not imply adjacency and strand labels depend on the reference orientation. We therefore introduce VenusGenome, a gene-resolution masked autoencoder that combines visible-only encoding with genome-aware attention incorporating contig boundaries, relative strand orientations, and gene positions. We pretrain this model on a curated corpus of over 1.7 million microbial genome assemblies from isolate and metagenomic sources, using frozen protein embeddings as gene-level inputs. Across genome-level phenotype prediction and gene-level tasks, the resulting frozen representations achieve the highest mean performance among the evaluated models on nine of eleven primary metrics. An exploratory gene-input perturbation analysis further links representation sensitivity to experimentally measured fitness defects. Ablations support the contribution of genome-aware attention and show the benefits of masked autoencoding over BERT-style masked modeling, with further gains from higher masking ratios.

Salinity stress represents a significant environmental challenge that affects plant growth, resulting in adverse impacts on crop productivity and overall yield. Salt accumulation in plants affects cellular homeostasis and provokes osmotic and ionic imbalance. It stimulates higher reactive oxygen species (ROS) production leading to oxidative stress damage and eventual cellular dysfunction. Beneficial microorganisms like plant growth-promoting rhizobacteria, arbuscular mycorrhizal fungi, endophytic fungi, and rhizosphere-associated microbes have recently emerged as resources for sustainable solutions to enhance plant resiliency against salinity stress. Their effects can involve osmotic adjustment, ion sequestration, oxidative-stress mitigation, phytohormone regulation, and rhizosphere-mediated stress buffering. The mechanisms to impart such benefits may include improved nutrients acquisition, assist in osmotic regulation, modulation of phytohormones signaling, and maintenance of cellular ion homeostasis. Often, beneficial microorganisms carry the potential to trigger both enzymatic and non-enzymatic antioxidant defense mechanisms. These responses contribute to oxidative-stress mitigation, yet, their interpretation requires contemplation of fine balance between oxidative damage and plant responses. For example, selected microbial inoculation studies have reported changes in the activity of crucial antioxidant enzymes, like superoxide dismutase, ascorbate peroxidase, catalase, and glutathione reductase. However, the magnitude and the direction of these responses are context dependent. Multi-omics approaches, including amplicon and shotgun metagenomics, metatranscriptomics, host transcriptomics, proteomics, and metabolomics, have expanded our understanding of the taxa, genes, transcripts, proteins, and metabolites associated with plant–microbe interactions under salinity. Therefore, here an effort has been made to present the evidential support for reported microbial effects and their proposed mechanisms of salt tolerance, with a focus on enzymatic and non-enzymatic defense mechanisms and the learnings of multi-omics studies. Frontier integrative mechanisms, including microbiome engineering, synthetic microbial consortia, and systems biology frameworks, are discussed for cultivating climate-resilient crops and sustainable agricultural systems in the salt-affected environments.